d81341b9b3
* Add rollout model unit tests * test: update rl rollout_model tests * test: fix cache_type_branches unsupported platform case * test: fix rl rollout_model test indent * Delete tests/spec_decode/test_mtp_proposer.py * chore: format test_rollout_model * chore: translate rollout test comments to English * test: guard rollout_model import by disabling auto registry * chore: reorder imports in rl rollout test * test: isolate env for RL rollout tests * style: format rollout RL tests with black * update * test: remove RL rollout unit tests causing collection issues * test: add lightweight rollout_model RL unit tests * fix(coverage): filter test file paths and handle collection failures - Only extract real test file paths (tests/.../test_*.py) from pytest collect output - Filter out ERROR/collecting prefixes to prevent garbage in failed_tests.log - Add proper error handling for pytest collection failures - Exit early if no test files can be extracted - Preserve collection error output for debugging * update * style: fix code style issues in test_rollout_model.py - Remove unused 'os' import - Remove trailing blank lines --------- Co-authored-by: YuBaoku <49938469+EmmonsCurse@users.noreply.github.com>
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Installation
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Quick Start
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Supported Models
FastDeploy : Inference and Deployment Toolkit for LLMs and VLMs based on PaddlePaddle
News
[2025-11] FastDeploy v2.3 is newly released! It adds deployment support for two major models, ERNIE-4.5-VL-28B-A3B-Thinking and PaddleOCR-VL-0.9B, across multiple hardware platforms. It further optimizes comprehensive inference performance and brings more deployment features and usability enhancements. For all the upgrade details, refer to the v2.3 Release Note.
[2025-09] FastDeploy v2.2: It now offers compatibility with models in the HuggingFace ecosystem, has further optimized performance, and newly adds support for baidu/ERNIE-21B-A3B-Thinking!
About
FastDeploy is an inference and deployment toolkit for large language models and visual language models based on PaddlePaddle. It delivers production-ready, out-of-the-box deployment solutions with core acceleration technologies:
- 🚀 Load-Balanced PD Disaggregation: Industrial-grade solution featuring context caching and dynamic instance role switching. Optimizes resource utilization while balancing SLO compliance and throughput.
- 🔄 Unified KV Cache Transmission: Lightweight high-performance transport library with intelligent NVLink/RDMA selection.
- 🤝 OpenAI API Server and vLLM Compatible: One-command deployment with vLLM interface compatibility.
- 🧮 Comprehensive Quantization Format Support: W8A16, W8A8, W4A16, W4A8, W2A16, FP8, and more.
- ⏩ Advanced Acceleration Techniques: Speculative decoding, Multi-Token Prediction (MTP) and Chunked Prefill.
- 🖥️ Multi-Hardware Support: NVIDIA GPU, Kunlunxin XPU, Hygon DCU, Iluvatar GPU, Enflame GCU, MetaX GPU, Intel Gaudi etc.
Requirements
- OS: Linux
- Python: 3.10 ~ 3.12
Installation
FastDeploy supports inference deployment on NVIDIA GPUs, Kunlunxin XPUs, Iluvatar GPUs, Enflame GCUs, Hygon DCUs and other hardware. For detailed installation instructions:
Get Started
Learn how to use FastDeploy through our documentation:
- 10-Minutes Quick Deployment
- ERNIE-4.5 Large Language Model Deployment
- ERNIE-4.5-VL Multimodal Model Deployment
- Offline Inference Development
- Online Service Deployment
- Best Practices
Supported Models
Learn how to download models, enable using the torch format, and more:
Advanced Usage
Acknowledgement
FastDeploy is licensed under the Apache-2.0 open-source license. During development, portions of vLLM code were referenced and incorporated to maintain interface compatibility, for which we express our gratitude.